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Posts from the ‘Cloud Computing’ Category

Internet of Things, Machine Learning & Robotics Are High Priorities For Developers In 2016

  • 200213603-00156.4% of developers are building robotics apps today.
  • 45% of developers say that Internet of Things (IoT) development is critical to their overall digital strategy.
  • 27.4% of all developers are building apps in the cloud today.
  • 24.7% are using machine learning for development projects.

These and many other insights are from the Evans Data Corporation Global Development Survey, Volume 1 (PDF, client access) published earlier this month. The methodology was based on interviews with developers actively creating new applications with the latest technologies. The Evans Data Corporation (EDC), International Panel of Developers, were sent invitations to participate and complete the survey online. 1,441 developers completed the survey globally. Please see page 17 of the study for additional details on the methodology.

Key takeaways from the study include the following:

  • Big Data analytics developers are spending the majority of their time creating Internet of Things (IoT).  The second-most popular Big Data analytics applications are in professional, scientific and technical services (10%), telecommunications (10%), and manufacturing (non-computer related) (9.6%). The following graphic provides an overview of where Big Data analytics developers are investing their time building new applications.

Best Describes App

  • Robotics (56.4%), Arts, Entertainment and Recreation (56.3%), and Automotive (52.9%) are the three most popular industries data mining app developers are focusing on today. Additional high priority industries include telecommunications (48.3%), Internet of Things (47.1%) and manufacturing (46.7%). A graphic from the study is shown below for reference.

Data Mining adoption

  • Nearly one-third (27.4%) of all app developers globally are planning to build new apps on the cloud. 66.9% expect to have a new cloud app within 12 months. Overall, 81.3% of all developers surveyed are building cloud apps today. The following graphic compares developers’ predicted timeframes for cloud app development over the next two years.

Plans for Apps In the Clouds

  • Better security (51.9%), more reliability (42%) and better user experience (41%) are the top three areas that motivate developers to move to new cloud platforms. Additional considerations include a better breadth of services (39.4%), networking and data center speed (37.8%), better pricing options (37.5%), better licensing structures (34.6%) and completeness of vision (30.9%). The following graphic compares the key factors that most motivate developers to switch cloud platforms.

key factors

  • 45% of developers say that Internet of Things (IoT) development is very important to their overall digital strategy. 7% say that IoT is somewhat important to their digital strategy. The study also found that 29.5% of all developers are creating Internet of Things (IoT) apps today. The following graphic illustrates the relative level of importance of IoT to developers’ digital strategies.

importance of IoT strategy

  • 41% say that cognitive computing and artificial intelligence (AI) are very important to their digital strategies. In speaking with senior executives at services firms, the opportunity to provide artificial intelligence-based services using a subscription model is gaining momentum, with many beginning to fund development projects to accomplish this on a global scale.

AI Importance

  • Most frequently created machine learning apps include those for the Internet of Things (11.4%), Professional, Scientific and Technical Services (10%), and Manufacturing (9.4%) industries.  Additional industries include telecommunications (8.3%), utilities/energy (8.1%), robotics (7.2%) and finance or insurance (6.8%). The following graphic breaks out the industries where machine learning app development is happening today.

Machine learning industries final

  • The majority of developers (84.2%) say that analytics is important for enabling their organizations to operate today. Of that group, 45.7% say that analytics are very important for their organizations to attain their goals.

2015 Gartner CRM Market Share Update

  • Worldwide customer relationship management (CRM) software totaled $26.3B in 2015, up 12.3% from $23.4B in 2014.
  • SaaS revenue grew 27% yr-over-yr, more than double overall CRM market growth in 2015.
  • Asia/Pacific grew the fastest of all regions globally, increasing 9% 2015, closely followed by greater China with 18.4% growth.

These and many other insights into the current state of the global CRM market are from Gartner’s Market Share Analysis: Customer Relationship Management Software, Worldwide, 2015 (PDF, client access) published earlier this month.  The top five CRM vendors accounted for 45% of the total market in 2015. Salesforce dominated in 2015, with a 21.1% annual growth rate and absolute growth of over $902M in CRM revenue, more than the next ten providers combined. Gartner found that Salesforce leads in revenue in the sales and customer service and support (CSS) segments of CRM, and is now third in revenue in the marketing segment. Gartner doesn’t address how analytics are fundamentally redefining CRM today, which is an area nearly every C-level and revenue team leader I’ve spoken with this year is prioritizing for investment. The following graphic and table compare 2015 worldwide CRM market shares.

CRM Market Share 2015

table 1

Adobe, Microsoft, and Salesforce Are Growing Faster Than The Market

Adobe grew the fastest between 2014 and 2015, increasing worldwide sales 26.9%. Salesforce continues to grow well above the worldwide CRM market average, increasing sales 21.1%. Microsoft increased sales 20% in the last year.  The worldwide CRM market grew 12.3% between 2014 and 2015.

Spending by vendor 2015

 Analytics, Machine Learning, and Artifical Intelligence Are The Future Of CRM

Advanced analytics, machine learning and artificial intelligence (AI) will revolutionize CRM in the next three years. Look to the five market leaders in 2015 to invest heavily in these areas with the goal of building patent portfolios and increasing the amount of intellectual property they own. Cloud-based analytics platforms offer the scale, speed of deployment, agility, and ability to rapidly prototype analytics workflows that support the next generation of CRM workflows. My recent post on SelectHub, Selecting The Best Cloud Analytics Platform: Trends To Watch In 2016, provides insights into how companies with investments in CRM systems are making decisions on cloud platforms today. Based on insights gained from discussions with senior management teams, I’ve put together an Intelligent Cloud Maturity Model that underscores why scalability of a cloud-based analytics platform is a must-have for any company.
cloud-maturity-model

Sources:  Gartner Says Customer Relationship Management Software Market Grew 12.3 Percent

Machine Learning Is Redefining The Enterprise In 2016

machine learning imageBottom line: Machine learning is providing the needed algorithms, applications, and frameworks to bring greater predictive accuracy and value to enterprises’ data, leading to diverse company-wide strategies succeeding faster and more profitably than before.

Industries Where Machine Learning Is Making An Impact  

The good news for businesses is that all the data they have been saving for years can now be turned into a competitive advantage and lead to strategic goals being accomplished. Revenue teams are using machine learning to optimize promotions, compensation and rebates drive the desired behavior across selling channels. Predicting propensity to buy across all channels, making personalized recommendations to customers, forecasting long-term customer loyalty and anticipating potential credit risks of suppliers and buyers are Figure 1 provides an overview of machine learning applications by industry.

machine learning industries

Source: Tata Consultancy Services, Using Big Data for Machine Learning Analytics in Manufacturing – TCS

Machine Learning Is Revolutionizing Sales and Marketing  

Unlike advanced analytics techniques that seek out causality first, machine learning techniques are designed to seek out opportunities to optimize decisions based on the predictive value of large-scale data sets. And increasingly data sets are comprised of structured and unstructured data, with the global proliferation of social networks fueling the growth of the latter type of data.  Machine learning is proving to be efficient at handling predictive tasks including defining which behaviors have the highest propensity to drive desired sales and marketing outcomes. Businesses eager to compete and win more customers are applying machine learning to sales and marketing challenges first.  In the MIT Sloan Management Review article, Sales Gets a Machine-Learning Makeover the Accenture Institute for High Performance shared the results of a recent survey of enterprises with at least $500M in sales that are targeting higher sales growth with machine learning. Key takeaways from their study results include the following:

  • 76% say they are targeting higher sales growth with machine learning. Gaining greater predictive accuracy by creating and optimizing propensity models to guide up-sell and cross-sell is where machine learning is making contributions to omnichannel selling strategies today.
  • At least 40% of companies surveyed are already using machine learning to improve sales and marketing performance. Two out of five companies have already implemented machine learning in sales and marketing.
  • 38% credited machine learning for improvements in sales performance metrics. Metrics the study tracked include new leads, upsells, and sales cycle times by a factor of 2 or more while another 41% created improvements by a factor of 5 or more.
  • Several European banks are increasing new product sales by 10% while reducing churn 20%. A recent McKinsey study found that a dozen European banks are replacing statistical modeling techniques with machine learning. The banks are also increasing customer satisfaction scores and customer lifetime value as well.

Why Machine Learning Adoption Is Accelerating

Machine learning’s ability to scale across the broad spectrum of contract management, customer service, finance, legal, sales, quote-to-cash, quality, pricing and production challenges enterprises face is attributable to its ability to continually learn and improve. Machine learning algorithms are iterative in nature, continually learning and seeking to optimize outcomes.  Every time a miscalculation is made, machine learning algorithms correct the error and begin another iteration of the data analysis. These calculations happen in milliseconds which makes machine learning exceptionally efficient at optimizing decisions and predicting outcomes.
The economics of cloud computing, cloud storage, the proliferation of sensors driving Internet of Things (IoT) connected devices growth, pervasive use of mobile devices that consume gigabytes of data in minutes are a few of the several factors accelerating machine learning adoption. Add to these the many challenges of creating context in search engines and the complicated problems companies face in optimizing operations while predicting most likely outcomes, and the perfect conditions exist for machine learning to proliferate.
The following are the key factors enabling machine learning growth today:

  • Exponential data growth with unstructured data being over 80% of the data an enterprise relies on to make decisions daily. Demand forecasts, CRM and ERP transaction data, transportation costs, barcode and inventory management data, historical pricing, service and support costs and accounting standard costing are just a few of the many sources of structured data enterprises make decisions with today.   The exponential growth of unstructured data that includes social media, e-mail records, call logs, customer service and support records, Internet of Things sensing data, competitor and partner pricing and supply chain tracking data frequently has predictive patterns enterprises are completely missing out on today. Enterprises looking to become competitive leaders are going after the insights in these unstructured data sources and turning them into a competitive advantage with machine learning.
  • The Internet of Things (IoT) networks, embedded systems and devices are generating real-time data that is ideal for further optimizing supply chain networks and increasing demand forecast predictive As IoT platforms, systems, applications and sensors permeate value chains of businesses globally, there is an exponential growth of data generated. The availability and intrinsic value of these large-scale datasets are an impetus further driving machine learning adoption.
  • Generating massive data sets through synthetic means including extrapolation and projection of existing historical data to create realistic simulated data. From weather forecasting to optimizing a supply chain network using advanced simulation techniques that generate terabytes of data, the ability to fine-tune forecasts and attain greater optimizing is also driving machine learning adoption. Simulated data sets of product launch and selling strategies is a nascent application today and one that shows promise in developing propensity models that predict purchase levels.
  • The economics of digital storage and cloud computing are combining to put infrastructure costs into freefall, making machine learning more affordable for all businesses. Online storage and public cloud instances can be purchased literally in minutes online with a credit card. Migrating legacy data off of databases where their accessibility is limited compared to cloud platforms is becoming more commonplace as greatest trust in secure cloud storage increases. For many small businesses who lack IT departments, the Cloud provides a scalable, secure platform for managing their data across diverse geographic locations.

Further reading

Companies Are Reimagining Business Processes with Algorithms. Harvard Business Review. February 8, 2016.  H. James Wilson, Allan Alter, Prashant Shukla. Source: https://hbr.org/2016/02/companies-are-reimagining-business-processes-with-algorithms

Domingos, P. (2012). A Few Useful Things to Know About Machine Learning. Communications Of The ACM, 55(10), 78-87.

Pyle, D., & San José, C. (2015). An executive’s guide to machine learning. Mckinsey Quarterly, (3), 44-53. Link: http://www.mckinsey.com/industries/high-tech/our-insights/an-executives-guide-to-machine-learning

Sales Gets A Machine-Learning Makeover.  MIT Sloan Management Review, May 17, 2016. H. James Wilson, Narendra Mulani, Allan Alter. Source: http://sloanreview.mit.edu/article/sales-gets-a-machine-learning-makeover/Sebag, M. (2014).

The Next Wave Of Enterprise Software Powered By Machine Learning.  TechCrunch, July 27, 2015. http://techcrunch.com/2015/07/27/the-next-wave-of-enterprise-software-powered-by-machine-learning/

What Every Manager Should Know About Machine Learning, Harvard Business Review,  July 7, 2015.  Link: https://hbr.org/2015/07/what-every-manager-should-know-about-machine-learning

What Is Machine Learning? Making The Complex Simple.  Mike Ferguson.  IBM Big Data & Analytics Hub. Link: http://www.ibmbigdatahub.com/blog/what-machine-learning

World Economic Forum White Paper Digital Transformation of Industries: In collaboration with Accenture Digital Enterprise, January 2016. Link: http://reports.weforum.org/digital-transformation-of-industries/wp-content/blogs.dir/94/mp/files/pages/files/digital-enterprise-narrative-final-january-2016.pdf

Yan, J., Zhang, C., Zha, H., Gong, M., Sun, C., Huang, J., & Yang, X. (2015, February). On machine learning towards predictive sales pipeline analytics. In Twenty-Ninth AAAI Conference on Artificial Intelligence.  Link: http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewFile/9444/9488

The Era Of The Intelligent Cloud Has Arrived

intelligent cloudBottom line: Enterprises are impatient to translate their investments in cloud apps and the insight they provide into business outcomes and solid results today.

The following insights are based on a series of discussions with C-level executives and revenue team leaders across several industries regarding their need for an Intelligent Cloud:

  • In the enterprise, the cloud versus on-premise war is over, and the cloud has won. Nearly all are embracing a hybrid cloud strategy to break down the barriers that held them back from accomplishing more.
  • None of the C-level executives I’ve spoken with recently are satisfied with just measuring cloud adoption. All are saying the want to measure business outcomes and gain greater insights into how they can better manage revenue and sales cycles.
  • Gaining access to every available legacy and 3rd party system using hybrid cloud strategies is the new normal. Having data that provides enterprise-wide visibility gives enterprises greater control over every aspect of their selling and revenue management processes. And when that’s accomplished, the insights gained from the Intelligent Cloud can quickly be turned into results.

Welcome to the Era of the Intelligent Cloud

The more enterprises seek out insights to drive greater business outcomes, the more it becomes evident the era of the Intelligent Cloud has arrived. C-level execs are looking to scale beyond descriptive analytics that defines past performance patterns.  What many are after is an entirely new level of insights that are prescriptive and cognitive. Getting greater insight that leads to more favorable business outcomes is what the Intelligent Cloud is all about. The following Intelligent Cloud Maturity Model summarizes the maturity levels of enterprises attempting to gain greater insights and drive more profitable business outcomes.

maturity model

 

Why The Intelligent Cloud Now?  

Line-of-business leaders across all industries want more from their cloud apps than they are getting today. They want the ability to gain greater insights with prescriptive and cognitive analytics. They’re also asking for new apps that give them the flexibility of changing selling behaviors quickly.  In short, everyone wants to get to the orchestration layer of the maturity model, and many are stuck staring into a figurative rearview mirror, using just descriptive data to plan future strategies.  The future of enterprise cloud computing is all about being able to deliver prescriptive and cognitive intelligence.
Consider the following takeaways:

 

Who Is Delivering The Intelligent Cloud Today?

Just how far advanced the era of the Intelligent Cloud is became apparent during the Microsoft Build Developer Conference last week in San Francisco.  A fascinating area discussed was Microsoft Cognitive Services and their implications on the Cortana Intelligence Suite. Microsoft is offering a test drive of Cognitive Services here. Combining Cognitive Services and the Cortana Intelligence Suite, Microsoft has created a framework for delivering the Intelligent Cloud. The graphic below shows the Cortana Analytics Suite.
Cortana suite

 

The Best Cloud Computing Companies And CEOs To Work For In 2016

careeer startEmployees would most recommend Zerto, FusionOps, Google, OutSystems, AppDirect, Sumo Logic, Cloudera, HyTrust, Tableau Software and Domo to their friends looking for a cloud computing company to work for in 2016. These and other insights are from an analysis completed today to determine the best cloud computing firms and CEOs to work for this year.

To keep the rankings and analysis completely impartial and fair, the latest Computer Reseller News list, The 100 Coolest Cloud Computing Vendors Of 2016 is the basis of the rankings. Cloud computing companies are among the most competitive there are about salaries, performance and sign-on bonuses and a myriad of perks and benefits. They are also attracting senior management teams that have strong leadership skills, many of whom are striving to create distinctive company cultures. The most popular request from Forbes readers are for recommendations of the best cloud computing companies to work for, and that’s what led to this analysis.

Using the 2016 CRN list as a baseline to compare the Glassdoor.com scores of the (%) of employees who would recommend this company to a friend and (%) of employees who approve of the CEO, the table below is provided. You can find the original data set here. There are many companies listed on the CRN list that doesn’t have than many or any entries on Glassdoor, and they are excluded from the rankings shown below but are in the original data set. If the image below is not visible in your browser, you can view the rankings here.

best cloud computing companies to work for in 2016 large

The highest rated CEOs on Glassdoor as of February 3rd, 2016 include the following:

  • Ziv Kedem, Zerto, 100%
  • Gary Meyers, FusionOps, 100%
  • Christian Chabot, Tableau Software, 100%
  • John Burton, Nintex, 100%
  • Rob Mee, Pivotal, 100%
  • Rajiv Gupta, Skyhigh Networks, 100%
  • Ken Shaw Jr., Infrascale, 100%
  • Beau Vrolyk, Engine Yard, 100%
  • Ramin Sayar, Sumo Logic, 99%
  • Sundar Pichai, Google, 98%
  • Lew Cirne, New Relic, 97%
  • Daniel Saks, AppDirect, 96%
  • James M. Whitehurst, Red Hat, 96%
  • Marc Benioff, Salesforce, 96%
  • Tom Kemp, Centrify, 95%
  • Jeremy Roche, FinancialForce, 95%

Roundup Of Internet of Things Forecasts And Market Estimates, 2015

  • Internet of things forecastCisco predicts the global IoT market will be $14.4T by 2022.
  • IC Insights predicts revenue from Industrial Internet IoT spending will increase from $6.4B in 2012 to $12.4B in 2015.
  • IoT in manufacturing market size is estimated to grow from $4.11B in 2015 to $13.49B by 2020, attaining a CAGR of 26.9%.

With the potential to streamline and deliver greater time and cost savings to a broad spectrum of enterprise tasks, opportunities for Internet of Things (IoT) adoption are proliferating. It’s encouraging to see so many industry-leading manufacturers, service providers, software and systems developers getting down to the hard work of making the vision IoT investments pay off.

Forecasting methodologies shifted in 2015 from the purely theoretical to being more anchored in early adoption performance gains. Gil Press wrote an excellent post on this topic Internet of Things By The Numbers: Market Estimates And Forecasts which continues to be a useful reference for market data and insights, as does his recent post, Internet Of Things (IoT) News Roundup: Onwards And Upwards To 30 Billion Connected Things.

Key takeaways from the collection of IoT forecasts and market estimates include the following:

ABI Research market estimates; Internet of Things market forecast

1`4 4 Trillion IoT Internet of Things market forecast

green graphic IoT Internet of Things market forecast

software BI; Internet of Things market forecast

  • IC Insights predicts revenue from Industrial Internet of Things spending will increase from $6.4B in 2012 to $12.4B in 2015, attaining a 17.98% CAGR. IC Insights predicts the Industrial Internet will lead all five categories of its forecast, with Connected Cities being the second-most lucrative, attaining a 13.16% CAGR in the forecast period. The research firm segments the industry into five IoT market categories: connected homes, connected vehicles, wearable systems, industrial Internet, and connected cities. Source: IC Insights Raises Growth Forecast for IoT.

revenue for IoT systems; Internet of Things market forecast

  • Manufacturing (27%), retail trade (11%), information services (9%), and finance and insurance (9%) are the four industries that comprise more than half the total value of the projected $14.4T market. The remaining 14 industries range between 7% percent and 1%. The following graphic based on Cisco’s analysis of the IoT market potential by industry and degree of impact. Cisco predicts Smart Factories will contribute $1.95T of the total value at stake by 2022. Source: Embracing the Internet of Everything To Capture Your Share of $14.4 Trillion, white paper published by Cisco.

top four industries IoT; Internet of Things market forecast

  • Intel Capital, Qualcomm Ventures, Foundry Group, Kleiner Perkins Caufield & Byers (KPCB), Andreessen Horowitz, Khosla Ventures, True Ventures and Cisco Investments are the leading IoT investors this year. Intel Capital is investing in a broad base of IoT-related technologies, encompassing 3D body-scanning and biometric sensors, wearable sand IoT infrastructure startups. Source: The Most Active VCs In The Internet Of Things And Their Investments In One Infographic. 

mot active IoT investors; Internet of Things market forecast

  • Vodafone’s latest Machine-to-Machine (M2M) study found that 37% of enterprises have projects targeted to go live in 2017. Vodafone defines M2M as technologies that connect machines, devices, and objects to the Internet, turning them into ‘intelligent’ assets that can communicate. M2M enables the Internet of Things. The following graphics compare M2M adoption trends from 2013 and 2015 and by industry. Source: 2015 Vodafone M2M Barometer Report (free, opt-in reqd., 36 pp.).

adoption of M2M 2013 2015; Internet of Things market forecast

adoption of M2M by industry; Internet of Things market forecast

growth iot; Internet of Things market forecast

  • New connections to the Internet of Things (IoT) will grow from about 1.7B in 2015 to nearly 3.1B in 2019. IoT applications will also fuel strong sales growth in optoelectronics, sensors/actuators, and discrete semiconductors, which are projected to reach $11.6B in 2019, attaining a CAGR of 26% during the forecast period. Source:  IC Insights Internet of Things Market to Nearly Double by 2019.

New Connections Internet of Things; Internet of Things market forecast

Internet of things forecast Microsoft; Internet of Things market forecast

McKinsey Institute Internet of Things; Internet of Things market forecast

  • IDC predicts that by 2018, 40% of the top 100 discrete manufacturers will rely on connected products to provide product as a service. 55% of discrete manufacturers are researching, piloting, or in production with IoT initiatives. By 2017, 50% of manufacturers will explore the viability of micrologistics networks to enable the promise of accelerated delivery for select products and customers. 65% of companies with more than ten plants will enable workers on the factory floor to make better business decisions through investments in operational intelligence. Source: IDC Manufacturing Insights Report courtesy of Cognizant, Transforming Manufacturing with the Internet of Things May 2015

 

Five Key Take-Aways From North Bridge’s Future Of Cloud Computing Survey, 2015  

  • bostonSaaS is the most pervasive cloud technology used today with a presence in 77.3% of all organizations, an increase of 9% since 2014.
  • IT is moving significant processing to the cloud with 85.9% of web content management, 82.7% of communications, 80% of app development and 78.9% of disaster recovery now cloud-based.
  • Seeking simple and clear relationships, over 50% of enterprises opt for online purchasing or direct to provider purchasing of cloud services. Online buying is projected to increase over the next two years up to 56%.
  • Vendor leadership/consolidation continues to take hold with 75% of enterprises using fewer than ten

These and many other insights are from North Bridge Growth Equity and Venture Partners’ Future of Cloud Computing Survey published on December 15th. North Bridge and Wikibon collaborated on the study, interviewing 952 companies across 38 different nations, with 65% being from the vendor community and 35% of enterprises evaluating and using cloud technologies in their operations  The slide deck is accessible on SlideShare here:

Key takeaways from the study include the following:

  1. Wikibon forecasts the SaaS is worth $53B market today and will grow at an 18% Compound Annual Growth Rate (CAGR) from 2014 to 2026. By 2026, the SaaS market will be worth $298.4B according to the Wikibon forecast. The fastest growing cloud technology segment is Platform-as-a-Service (PaaS), which is valued at $2.3B today, growing at a CAGR of 38% from 2014 to 2026.  Infrastructure-as-a-Service (IaaS) has a market value of $25B and is growing at a 19% CAGR in the forecast period.  Please see the graphic from the report below and a table from Wikibon’s excellent study, Public Cloud Market Forecast 2015-2026 by Ralph Finos published in August.

SaaS Graphic from North Bridge study

 

Public Cloud Vendor Revenue Projection

  1. Cloud-based applications are becoming more engrained in core business processes across enterprises. The study found that enterprises are migrating significant processing, systems of engagement and systems of insight to the cloud beyond adoption levels of the past.  81.3% of sales and marketing, 79.9% of business analytics, 79.1% of customer service and 73.5% of HR & Payroll activities have transitioned to the cloud. The impact on HR is particularly noteworthy as in 2011; it was the third least likely sector to be disrupted by cloud computing.
  1. 78% of enterprises expect their SaaS investments to deliver a positive Return on Investment (ROI) in less than three months. 58% of those enterprises who have invested in Platform-as-a-Service (PaaS) expect a positive ROI in less than three months.
  1. Top inhibitors to cloud adoption are security (45.2%), regulatory/compliance (36%), privacy (28.7%), lock-in (25.8%) and complexity (23.1%). Concerns regarding interoperability and reliability have fallen off significantly since 2011 (15.7% and 9.9% respectively in 2015).
  1. Total private financing for cloud and SaaS startup has increased 4X over the last five years. North Bridge and Wikibon found that average deal size rose 1.8X in the same period. The following graphic provides an overview of cloud and SaaS finance trends from 2010 to present.

cloud and saas financing

 

5 Insights & Predictions On Disruptive Tech From KPMG’s 2015 Global Innovation Survey

  • cloud computing survey 215% of U.S. tech leaders see biotech/digital health/healthcare IT as the most disruptive consumer-driven technology in the next three years.
  • 13% of U.S. tech leaders predict data and analytics will be the most disruptive enterprise technology in three years.
  • Global tech leaders predict cloud computing (11%), mobile platforms and apps (9%), Internet of Things (IoT)/machine-to-machine (M2M) (9%) and data and analytics (9%) will be the most disruptive technologies over the next three years.

These and many other insights are from the fourth annual 2015 Global Technology Innovation Survey released via webcast by KPMG last month. KPMG surveyed 832 technology industry business leaders globally, with the majority of being C-level executives (87%). Respondents were selected from a broad spectrum of businesses including tech industry startups, mid- and large-scale enterprises, angel investors and venture capital firms. For an in-depth explanation of the survey methodology, please see slides 6 and 7 of the webinar presentation. The goals of the survey include spotting disruptive technologies, identifying tech innovation barriers and opportunities, and tracking emerging tech innovation hubs.

The five insights and predictions from the report include the following:

  • Global tech leaders predict cloud computing (11%), mobile platforms and apps (9%), Internet of Things (IoT)/M2M (9%) and data and analytics (9%) will be the most disruptive technologies over the next three years.  U.S. tech leaders predict biotech/digital health/healthcare IT (15%), data and analytics (14%) and cloud computing (14%) will be the three most disruptive technologies over the next three years.  Chinese tech leaders predict artificial intelligence/cognitive computing (15%) will be the most disruptive technology impacting the global business-to-consumer (B2C) marketplace.

tech driving consumer technologies

  • The three most disruptive technologies predicted to drive business transformation in enterprises over the next three years in the U.S. include cloud computing (13%), data and analytics (13%), and cyber security (10%). Japanese tech leaders predict artificial intelligence/cognitive computing will have the greatest effect (23%), and 14% of Chinese tech leaders predict the Internet of Things/M2M (14%) will have the greatest impact on business transformation in their country.  The following table compares global tech leader’s predictions of which technologies will disrupt enterprises the most and drive business transformation over the next three years.

business transformation

  • Improving business efficiencies/higher productivity, and faster innovation cycles (both 20%) are top benefits tech leaders globally are pursuing with IoT strategies. The point was made on the webinar that in Asia, consumers are driving greater adoption of IoT-based devices to a richer contextual customer experience. Greatest challenges globally to adopting IoT is technology complexity (22%), lack of experience in the new technology or business model (16%), and both displacement of the existing tech roadmap and security (both 13%).       

IoT in the enteprrise

  • Analytics are most often adopted to gain faster innovation cycles (25%), improved business efficiencies and higher productivity (17%) and more effective R&D (13%).  The greatest challenges are technology complexity (20%) and lack of experience in the new technology or business model (19%),

data and analytics KPMG Survey

  • Tech leaders predict the greatest potential revenue growth for IoT in the next three years is in consumer and retail markets (22%).  IoT/M2M is also expected to see significant revenue growth in technology industries (13%), aerospace and defense (10%), and education (9%).  The following graphic compares tech leader’s predictions of the industries with the greatest potential revenue growth (or monetization potential) in the next three years.

Emerging Tech IoT monetization

 

Sources:

Tech Innovation Global Webcast presenting the findings of KPMG’s 2015 Global Technology Innovation Survey

KPMG Survey: Top Disruptive Consumer Tech – AI In China, Healthtech In U.S., 3-D Printing In EMEA

 

Salesforce On The State Of Analytics, 2015

  • analytics predictions 2015Between 2015 and 2020, the number of data sources analyzed by enterprises will jump 83%.
  • 9 out of 10 enterprise leaders believe analytics is absolutely essential or very important to their overall business strategies and operational outcomes.
  • 54% of marketers say marketing analytics is absolutely critical or very important to creating a cohesive customer journey.
  • High performing enterprises are 5.4x more likely than underperformers to primarily use analytics tools to gain strategic insights from Big Data.

These and many other interesting insights are from the 2015 State of Analytics study from Salesforce Research. Salesforce conducted the study in mid-2015, generating 2,091 responses from business leaders from enterprises (not limited to Salesforce customers). Geographies included in the study include the U.S., Canada, Brazil, U.K., France, Germany, Japan, and Australia.  While Salesforce is a leading provider of analytics, the report strives to deliver useful insights beyond just endorsing their product direction.

10 insights and predictions on the state of analytics include the following:

  • Between 2015 and 2020, the number of data sources analyzed will jump 83%. Salesforce Research found that the number of data sources actively analyzed by businesses has grown just 20% in the last five years. This is projected to accelerate rapidly, attaining a compound annual growth rate of 120% in the 10-year forecast period. High performing enterprises will be relying on a projected 50 different data sources by 2020, leading all performance categories tracked in the study.

data explosion

  • Relying on manual processes to get all the data in one view (53%) is one of the greatest challenges enterprises face today. Additional factors driving enterprises to integrate more data sources into their analytics applications include finding that too much data is left unanalyzed (53%), spending too much time updating spreadsheets (52%), and analysis is performance by business analysts, not end users of the data (50%).  All of these factors and those shown in the graphic below form the catalyst that is driving greater legacy, 3rd party and broader enterprise data integration into analytics applications.

lack of automation

  • 9 out of 10 enterprise leaders believe analytics is absolutely essential or very important to their overall business strategies and operational outcomes. In addition, 84% of high performers are projecting that the importance of analytics will increase substantially or somewhat in the next two years. 65% of all business leaders surveyed are predicting that the importance of analytics will increase substantially or somewhat in the next two years.

analytics is critical to driving business strategy

  • High performing enterprises are 4.6x more likely than underperformers to agree that data is driving their business decisions. In addition, 60% of high performing enterprises’ leaders agree with the statement that their organizations have moved beyond numbers keeping score to data driving business decisions. Salesforce Research also found that 43% of high performers rely on empirical data, developing hypotheses and then experimenting and observing the outcomes before making a decision.

data drives decisions

  • Driving operational efficiencies and facilitating growth (both 37%) are the two areas enterprises are initially focused on with analytics today.  Once analytics apps are delivering insights and are part of daily workflows, enterprises expand their use into optimizing operational processes (35%), identifying new revenue streams (33%) and predicting customer behavior (32%). The following graphic provides a comparison of the top ten use cases.

analytics every corner

  • High performance enterprises consistently analyze more than 17 different kinds of data across their analytics apps.  In contrast, underperforming organizations only analyze 10 different data sources, and moderate performers, 15. The following graphic provides an overview of the top ten most-used sources of data.

companies track a wide variety of data

  • High performers are 3.5x more likely than underperformers to extensively use mobile reporting tools to analyze data wherever they are. 55% of high performing enterprises are more likely to be extensively using mobile reporting tools to analyze data.  The following graphic compares mobile analytics adoption across high, medium and low performing enterprises.

top teams tap mobile analytics

  • Speed of deployment (68%), ease of use for business users (65%) and self-service and data discovery tools (61%) are the three top three priorities leaders place on selecting new analytics apps.  Mobile capabilities to explore and share data (56%) and cloud deployment (54%) are the fourth and fifth factors leaders mentioned.  The following graphic compares the decision factors that go into selecting an analytics app.

decision factor analytics app

  • Industries who have the greater analytics adoption today (over 50% of users active on apps and tools) include high tech (36%) and financial services (32%). Automotive (30%) and media & communications (30%) also have attained significant adoption.

adoption

  • High performing enterprises are 5.4x more likely than underperformers to primarily use analytics tools to gain strategic insights from Big Data. Leaders in high performance enterprises see the value of Big Data (76%) to a much greater extent than their lower performing counterparts (14%).   High performing enterprises are 3.1x more likely than underperformers to be confident in ability to manage data from internal systems, customers, and third parties.

Key Take-Aways From The 2015 Pacific Crest SaaS Survey

  • Cloud Computing M&A40% of SaaS companies are using Amazon Web Services (AWS) to deliver their apps today.
  • Median subscription gross margins for SaaS companies in 2015 are 78%.
  • Overall, SaaS companies are projecting median revenue growth of 46% in 2015.
  • Channel sales and inside sales strategies delivered the highest revenue growth rates in 2014.
  • Companies in the $5M – $7.5M range achieved 70% revenue growth in 2014, surpassing the median 36% growth rate last year.

These and many other insights are from the 2015 Pacific Crest SaaS Survey published by David Skok of Matrix Partners in collaboration with Pacific Crest Securities. You can download a free copy of Part I of the study here (PDF, opt-in, 72 pp). 305 SaaS companies were interviewed, 31% from international locations and 69% from North America.  David Skok and Pacific Crest Securities will publish Part 2 of the results in the near future. SaaS Metrics 2.0 – Detailed Definitions provides a useful reference for many of the SaaS metrics mentioned in the study.

This year’s survey attracted an eclectic base of respondents, with median revenues of $4M a year, with 133 companies reporting less than $5M, and 57 over $25M. Annual Contract Value (ACV) across all respondents is $21K, with 17% of respondents reporting ACVs over $100K.  Please see pages 3 & 4 of the study for a description of the methodology. Key take-aways from the study include the following:

  • SaaS GAAP revenue growth is accelerating in 2014 and is projected to increase further in 2015 from 44% to 46%. Median revenue growth in 2014 for all survey respondents was 44%, with the aggregate projected growth for 2015 reaching 46%. When SaaS companies with less than $2.5M in revenues are excluded, median GAAP growth was 35% in 2014 and is expected to reach that same level in 2015.

grow SaaS Revenue

 

  • SaaS companies with mixed customer strategies are growing at 57% a year.  Excluding respondent companies with less than $2.5M in revenues, a mixed customer strategy dominates all others. Concentrating on enterprises and small & medium businesses (SMBs) both drove 33% revenue growth of respondent companies this year.

median growth rate as a function of customer

 

  • 40% of SaaS companies are using Amazon Web Services (AWS) to deliver their apps today. AWS is projected to increase to 44% three years from now, with Microsoft Azure increasing from 3% today to 6% in 3 years.

SaaS Delivered

 

  • 41% of all SaaS companies surveyed rely primarily on field sales.  Factoring out the companies with less than $2.5M in revenue, field sales accounts for 32%.

primary mode of distribution

 

  • Field sales dominates as the most effective sales strategy when median deal sizes are $50K or more. In contrast, inside sales dominates $5K to $15K deal sizes, and the Internet dominates deal sizes less than $1K.  The following graphic provides insights into the primary mode of sales by median initial contract size.

mode by initial contract size

 

  • 16% of new Average Contract Value (ACV) sales is from upsells, with the largest companies being the most effective at this selling strategy. One of the strongest catalysts of a SaaS companies’ growth is the ability to upsell customers to a higher ACV, generating significantly greater gross margin in the process. SaaS companies with revenues between $40M to $75M increase their ACV by 32% using upsells. Larger SaaS companies with over $75M in sales generate 28% additional ACV with upsell strategies.

ACV Value

 

  • The highest growth SaaS companies are relying on upsells to fuel higher ACV.  There is a significant difference between the highest and lowest growth SaaS companies when it comes to upsell expertise and execution.  The following graphic provides an overview by 2014 GAAP revenue category of percent of ACV attributable to upsells.

fast upsell

 

  • 60% are driving revenues with “Try Before You Buy” strategies, with 30% generating the majority of their revenues using this approach.  On contrast, only 30% of companies generate revenues and ACV from freemium.

freemium

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